Satellite estimates of net community production based on O2/Ar observations and comparison to other estimates
We present two statistical algorithms for predicting global oceanic net community production (NCP) from satellite observations. To calibrate these two algorithms, we compiled a large data set of in situ O2/Ar‐NCP and remotely sensed observations, including sea surface temperature (SST), net primary...
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ftoceanrep:oai:oceanrep.geomar.de:49676 2023-05-15T18:25:27+02:00 Satellite estimates of net community production based on O2/Ar observations and comparison to other estimates Li, Zuchuan Cassar, Nicolas 2016-05 text https://oceanrep.geomar.de/id/eprint/49676/ https://oceanrep.geomar.de/id/eprint/49676/1/2015GB005314.pdf https://doi.org/10.1002/2015GB005314 en eng AGU (American Geophysical Union) https://oceanrep.geomar.de/id/eprint/49676/1/2015GB005314.pdf Li, Z. and Cassar, N. (2016) Satellite estimates of net community production based on O2/Ar observations and comparison to other estimates. Global Biogeochemical Cycles, 30 (5). pp. 735-752. DOI 10.1002/2015GB005314 <https://doi.org/10.1002/2015GB005314>. doi:10.1002/2015GB005314 info:eu-repo/semantics/restrictedAccess Article PeerReviewed 2016 ftoceanrep https://doi.org/10.1002/2015GB005314 2023-04-07T15:50:27Z We present two statistical algorithms for predicting global oceanic net community production (NCP) from satellite observations. To calibrate these two algorithms, we compiled a large data set of in situ O2/Ar‐NCP and remotely sensed observations, including sea surface temperature (SST), net primary production (NPP), phytoplankton size composition, and inherent optical properties. The first algorithm is based on genetic programming (GP) which simultaneously searches for the optimal form and coefficients of NCP equations. We find that several GP solutions are consistent with NPP and SST being strong predictors of NCP. The second algorithm uses support vector regression (SVR) to optimize a numerical relationship between O2/Ar‐NCP measurements and satellite observations. Both statistical algorithms can predict NCP relatively well, with a coefficient of determination (R2) of 0.68 for GP and 0.72 for SVR, which is comparable to other algorithms in the literature. However, our new algorithms predict more spatially uniform annual NCP distribution for the world's oceans and higher annual NCP values in the Southern Ocean and the five oligotrophic gyres Article in Journal/Newspaper Southern Ocean OceanRep (GEOMAR Helmholtz Centre für Ocean Research Kiel) Southern Ocean Global Biogeochemical Cycles 30 5 735 752 |
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Open Polar |
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OceanRep (GEOMAR Helmholtz Centre für Ocean Research Kiel) |
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ftoceanrep |
language |
English |
description |
We present two statistical algorithms for predicting global oceanic net community production (NCP) from satellite observations. To calibrate these two algorithms, we compiled a large data set of in situ O2/Ar‐NCP and remotely sensed observations, including sea surface temperature (SST), net primary production (NPP), phytoplankton size composition, and inherent optical properties. The first algorithm is based on genetic programming (GP) which simultaneously searches for the optimal form and coefficients of NCP equations. We find that several GP solutions are consistent with NPP and SST being strong predictors of NCP. The second algorithm uses support vector regression (SVR) to optimize a numerical relationship between O2/Ar‐NCP measurements and satellite observations. Both statistical algorithms can predict NCP relatively well, with a coefficient of determination (R2) of 0.68 for GP and 0.72 for SVR, which is comparable to other algorithms in the literature. However, our new algorithms predict more spatially uniform annual NCP distribution for the world's oceans and higher annual NCP values in the Southern Ocean and the five oligotrophic gyres |
format |
Article in Journal/Newspaper |
author |
Li, Zuchuan Cassar, Nicolas |
spellingShingle |
Li, Zuchuan Cassar, Nicolas Satellite estimates of net community production based on O2/Ar observations and comparison to other estimates |
author_facet |
Li, Zuchuan Cassar, Nicolas |
author_sort |
Li, Zuchuan |
title |
Satellite estimates of net community production based on O2/Ar observations and comparison to other estimates |
title_short |
Satellite estimates of net community production based on O2/Ar observations and comparison to other estimates |
title_full |
Satellite estimates of net community production based on O2/Ar observations and comparison to other estimates |
title_fullStr |
Satellite estimates of net community production based on O2/Ar observations and comparison to other estimates |
title_full_unstemmed |
Satellite estimates of net community production based on O2/Ar observations and comparison to other estimates |
title_sort |
satellite estimates of net community production based on o2/ar observations and comparison to other estimates |
publisher |
AGU (American Geophysical Union) |
publishDate |
2016 |
url |
https://oceanrep.geomar.de/id/eprint/49676/ https://oceanrep.geomar.de/id/eprint/49676/1/2015GB005314.pdf https://doi.org/10.1002/2015GB005314 |
geographic |
Southern Ocean |
geographic_facet |
Southern Ocean |
genre |
Southern Ocean |
genre_facet |
Southern Ocean |
op_relation |
https://oceanrep.geomar.de/id/eprint/49676/1/2015GB005314.pdf Li, Z. and Cassar, N. (2016) Satellite estimates of net community production based on O2/Ar observations and comparison to other estimates. Global Biogeochemical Cycles, 30 (5). pp. 735-752. DOI 10.1002/2015GB005314 <https://doi.org/10.1002/2015GB005314>. doi:10.1002/2015GB005314 |
op_rights |
info:eu-repo/semantics/restrictedAccess |
op_doi |
https://doi.org/10.1002/2015GB005314 |
container_title |
Global Biogeochemical Cycles |
container_volume |
30 |
container_issue |
5 |
container_start_page |
735 |
op_container_end_page |
752 |
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1766206917402689536 |